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<a href="#pub-methods">Public Member Functions</a> &#124;
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<p><code>#include &lt;<a class="el" href="linear__regression_8h_source.html">linear_regression.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:ad05db970a80e02c1f79a3db4d035d006"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#ad05db970a80e02c1f79a3db4d035d006">LinearRegression</a> (int numSensors, int numMotors)</td></tr>
<tr class="separator:ad05db970a80e02c1f79a3db4d035d006"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab0ca8360d4aaab247b8156444ad988f8"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#ab0ca8360d4aaab247b8156444ad988f8">~LinearRegression</a> ()</td></tr>
<tr class="separator:ab0ca8360d4aaab247b8156444ad988f8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a47fb0dee714ba8d0cffe01b75c728613"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a47fb0dee714ba8d0cffe01b75c728613">setDesiredOut</a> (Matrix sensors)</td></tr>
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<tr class="memitem:adb0c59e076a7946d6fb6ceb39114f5b5"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#adb0c59e076a7946d6fb6ceb39114f5b5">setState</a> (Matrix motors)</td></tr>
<tr class="separator:adb0c59e076a7946d6fb6ceb39114f5b5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a400c19eb43f36f33aa65fa7d13fd6b76"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a400c19eb43f36f33aa65fa7d13fd6b76">predict</a> ()</td></tr>
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<tr class="memitem:a57ef4db1ebbe11cffaf1874f04cd85eb"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a57ef4db1ebbe11cffaf1874f04cd85eb">getModelMatrix</a> (Matrix <a class="el" href="classLinearRegression.html#aacbab3190af48c974ef498394a206ec4">inputs</a>)</td></tr>
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Private Member Functions</h2></td></tr>
<tr class="memitem:af89bc122f48bd024e7bef82ae2e8d84f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#af89bc122f48bd024e7bef82ae2e8d84f">updateWeights</a> ()</td></tr>
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Private Attributes</h2></td></tr>
<tr class="memitem:aa1c2a397b9d243bd63998eab08e952ab"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#aa1c2a397b9d243bd63998eab08e952ab">inputNum</a></td></tr>
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<tr class="memitem:ac0ae52ea38fd61019d6d94845229a6f2"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#ac0ae52ea38fd61019d6d94845229a6f2">outputNum</a></td></tr>
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<tr class="memitem:aa783c40e02b8661f494c20ec8c724ca6"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#aa783c40e02b8661f494c20ec8c724ca6">forgettFactor</a></td></tr>
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<tr class="memitem:a732cefc5759ef27e2ddacda4bed7e652"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a732cefc5759ef27e2ddacda4bed7e652">predictedOut</a></td></tr>
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<tr class="memitem:a06b23260af70aa7491394b081230c538"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a06b23260af70aa7491394b081230c538">invCorrelation</a></td></tr>
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<tr class="memitem:aacbab3190af48c974ef498394a206ec4"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#aacbab3190af48c974ef498394a206ec4">inputs</a></td></tr>
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<tr class="memitem:a47d66c19a4d50116875969be51248a6c"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a47d66c19a4d50116875969be51248a6c">regression_coeff</a></td></tr>
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<tr class="memitem:a4955fea50332c1e52dff369a0ba9f1a0"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#a4955fea50332c1e52dff369a0ba9f1a0">desiredOut</a></td></tr>
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<tr class="memitem:aabe743e668a3ae0d82f2da8978deb186"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classLinearRegression.html#aabe743e668a3ae0d82f2da8978deb186">error</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>File: <a class="el" href="linear__regression_8h.html">linear_regression.h</a></p>
<p>Class that implements a linear regression as predictor. The regression coefficients are recurrently updated based on the recursive least squares. As presented in Chapter 3.2.1 of the thesis.</p>
<dl class="section author"><dt>Author</dt><dd>: Athanasios Polydoros </dd></dl>
<dl class="section version"><dt>Version</dt><dd>: 1.0 Created on 03 July 2013, 20:35 </dd></dl>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
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          <td class="memname">LinearRegression::LinearRegression </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>numSensors</em>, </td>
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          <td class="paramname"><em>numMotors</em>&#160;</td>
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<p>Class constructor that initialise the members of class. Has to be called in the controller's init method.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">num_Sensors</td><td>The number of robot sensors, used as network's outputs </td></tr>
    <tr><td class="paramname">num_Motors</td><td>The number of motors, used as inputs </td></tr>
  </table>
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<p>Class destructor </p>

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<h2 class="groupheader">Member Function Documentation</h2>
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          <td class="memname">Matrix LinearRegression::getModelMatrix </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>inputs</em>)</td><td></td>
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<p>Calculate the jacobian matrix. In the case of regression, it is simply the matrix of regression coefficients without the bias weight.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">the</td><td>input nodes values </td></tr>
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  </dd>
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<dl class="section return"><dt>Returns</dt><dd>MxM Jacobian matrix </dd></dl>

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<p>Predicts the future sensor values based on the current motor commands (inputs).</p>
<dl class="section return"><dt>Returns</dt><dd>Matrix that contains the predicted sensory values. </dd></dl>

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          <td class="memname">void LinearRegression::setDesiredOut </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>sensors</em>)</td><td></td>
          <td></td>
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<p>Sets the desired output. This method is called within controler's method : learn() before the method</p>
<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classLinearRegression.html#a400c19eb43f36f33aa65fa7d13fd6b76">predict()</a></dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">sensor</td><td>the robot's desired sensory values </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">void LinearRegression::setState </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>motors</em>)</td><td></td>
          <td></td>
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<p>Set the current inputs.</p>
<p>This method is called within controler's method : learn() before the method</p>
<dl class="section see"><dt>See Also</dt><dd><a class="el" href="classLinearRegression.html#a400c19eb43f36f33aa65fa7d13fd6b76">predict()</a></dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">motors</td><td>The values of robot's motors </td></tr>
  </table>
  </dd>
</dl>

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          <td>(</td>
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<p>Update weights based on recursive least square learning rule. </p>

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<h2 class="groupheader">Member Data Documentation</h2>
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<p>The desired values of the output nodes </p>

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<p>The prediction error </p>

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<p>forgeting factor, set to 1, no forgetting </p>

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<p>The number of inputs </p>

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<p>The values of the inuts </p>

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<p>The inverse of inputs corellation, used in recursive lest square formula </p>

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<p>The number of outpouts (targets) </p>

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          <td class="memname">Matrix LinearRegression::predictedOut</td>
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<p>The predicted values of outputs </p>

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          <td class="memname">Matrix LinearRegression::regression_coeff</td>
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<p>The regression coefficients </p>

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<hr/>The documentation for this class was generated from the following files:<ul>
<li><a class="el" href="linear__regression_8h_source.html">linear_regression.h</a></li>
<li><a class="el" href="linear__regression_8cpp.html">linear_regression.cpp</a></li>
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